PREDICTION OF GENE LOCATIONS USING STOCHASTIC MODELS

拟议项目的总体目标是开发一个改进的 预测新测序DNA中基因位置的计算机算法。 这个问题是众所周知的，但仍然远远没有成功 解决了 一种新的方法来解决这个问题，利用剪接位点和 随机形式的编码/非编码DNA序列信息 模型 必须实现几个具体目标： 1)最有效的非平稳马尔可夫链模型的类型 蛋白质编码区（外显子）必须根据以下条件选择： 统计分析先前编译的真核生物学习集 根据拟合优度测试的DNA。 同时，最有效的类型 非编码DNA序列（内含子）的普通马尔可夫链模型 必须基于内含子学习集的分析来确定。 2)一组改进的参数需要计算的价值， 辨别能量（估计剪接的相对活性 站点）将从已知剪接的扩展学习集合中提取 网站. 3)剪接位点随机模型和编码/非编码DNA模型 序列（在贝叶斯类型算法中连接在一起， DNA片段的编码潜力的值）必须被组合， 作为一种新的多阶段基因鉴定方法 地点 4)在评估了该方法的准确性之后， 阈值，提高计算性能，并创建一个 交互环境的方法，软件将作出 提供给科学界。

The general objective of the proposed project is to develop an improved computer algorithm for predicting gene locations in newly sequenced DNA. This problem is well known but still far from being successfully resolved. A new approach to the problem utilizes both splicing site and coding/noncoding DNA sequence information in the form of stochastic models. There are several specific aims that have to be achieved: 1) The most efficient type of nonstationary Markov chain model of the protein coding region (exons) has to be chosen on the basis of statistical analysis of previously compiled learning sets of eukaryotic DNA according to the goodness-of-fit test. Also, the most efficient type of an ordinary Markov chain model of noncoding DNA sequences (introns) has to be determined based on the analysis of the intron learning set. 2) An improved set of parameters needed for calculation of the value of the discrimination energy (estimating the relative activity of a splicing site) will be extracted from an expanded learning set of known splicing sites. 3) Splicing site stochastic models and models of coding/noncoding DNA sequences (joined together in a Bayes type algorithm finding out the value of the coding potential of a DNA fragment) have to be combined and enhanced as a new multistage method for the identification of gene locations. 4) After evaluating the method's accuracy, scaling of decision making thresholds, improving computational performance, and creating an interactive environment for the method, the software will be made available to the scientific community.